conditional neural process: Difference between revisions
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(Created page with "== Introduction == To train a model effectively, deep neural networks require large datasets. To mitigate this data efficiency problem, learning in two phases is one approach...") |
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of a generic domain without committing to a specific learning task; the second phase learns a function for a specific task, but does so using only a small number of data points by exploiting the domain-wide statistics already learned. | of a generic domain without committing to a specific learning task; the second phase learns a function for a specific task, but does so using only a small number of data points by exploiting the domain-wide statistics already learned. | ||
For example, consider a data set ${x_i, y_i}$ | For example, consider a data set $${x_i, y_i}$$ | ||
Revision as of 16:30, 18 November 2018
Introduction
To train a model effectively, deep neural networks require large datasets. To mitigate this data efficiency problem, learning in two phases is one approach : the first phase learns the statistics of a generic domain without committing to a specific learning task; the second phase learns a function for a specific task, but does so using only a small number of data points by exploiting the domain-wide statistics already learned.
For example, consider a data set $${x_i, y_i}$$